Job Details

PI Data Engineering team (part of Personal Investing Technology BU) is focused on delivery data and ML solutions for the organization. As part of this team, you will be responsible for building advanced analytics solutions using various cloud technologies and collaborating with Data Scientists to robustly scale up ML Models to large volumes in production.
The Expertise You Have
Has Bachelor’s or Master’s Degree in a technology related field (e.g. Engineering, Computer Science, etc.).
Experience in Object Oriented Programming (Java, Scala, Python), SQL, Unix scripting or related programming languages and exposure to some of Python’s ML ecosystem (numpy, panda, sklearn, tensorflow, etc.).
Experience in building cloud native applications using AWS services like S3, RDS, CFT, SNS, SQS, Step functions, Event Bridge, cloud watch etc.,
Experience with building data pipelines in getting the data required to build, deploy and evaluate ML models, using tools like Apache Spark, AWS Glue or other distributed data processing frameworks.
Data movement technologies (ETL/ELT), Messaging/Streaming Technologies (AWS SQS, Kinesis/Kafka), Relational and NoSQL databases (DynamoDB, EKS, Graph database), API and in-memory technologies.
Strong knowledge of developing highly scalable distributed systems using Open-source technologies.
5+ years of proven experience in implementing Big data solutions in data analytics space.
Experience in developing ML infrastructure and MLOps in the Cloud using AWS Sagemaker.
Extensive experience working with machine learning models with respect to deployment, inference, tuning, and measurement required.
Experience with CI/CD tools (e.g., Jenkins or equivalent), version control (Git), orchestration/DAGs tools (AWS Step Functions, Airflow, Luigi, Kubeflow, or equivalent).
Solid experience in Agile methodologies (Kanban and SCRUM).